CN111340821A - Deviation detection method of brain structure network based on module connection - Google Patents

Deviation detection method of brain structure network based on module connection Download PDF

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CN111340821A
CN111340821A CN202010105253.0A CN202010105253A CN111340821A CN 111340821 A CN111340821 A CN 111340821A CN 202010105253 A CN202010105253 A CN 202010105253A CN 111340821 A CN111340821 A CN 111340821A
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李丹丹
王彬
崔晓红
相洁
曹锐
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Taiyuan University of Technology
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Abstract

The invention discloses a deflection detection method of a brain structure network based on module connection, which comprises the following steps: preprocessing the diffusion tensor imaging, and performing region segmentation on the preprocessed diffusion tensor imaging according to the selected standardized brain atlas; reconstructing fibers of the whole brain based on the tracking end condition by adopting a deterministic fiber bundle tracking algorithm, and calculating the fiber bundle quantity and partial anisotropy index of every two brain regions and the Surface area of each brain region to obtain a fiber bundle quantity matrix FN, a partial anisotropy index matrix FA and a brain region Surface area matrix Surface of the brain regions; compared with the traditional laterality detection method, the laterality detection method of the brain structure network based on module connection, disclosed by the invention, fuses network modularity and laterality, and can more effectively and accurately mine laterality of local network indexes of the brain, thereby providing certain help for exploring a brain working mechanism.

Description

Deviation detection method of brain structure network based on module connection
Technical Field
The invention belongs to the field of brain structure imaging and the technical field of brain network topological structure analysis, and particularly relates to a deflection detection method of a brain structure network based on module connection.
Background
Laterality refers to structural and functional asymmetry of the left and right hemispheres of the brain, which arises from embryonic stage and changes with age, environment and experience, and is a fundamental feature of human brain development. The laterality of brain structures mainly includes white matter volume of brain, white matter integration, and the like. Brain functional laterality refers to the difference in spatiotemporal processing, speech, motor, and cognitive control functions in the left and right hemispheres. Many neurological studies have revealed that areas of normal human presence of white matter laterality are often accompanied by laterality of brain function. As shown in the research, the white matter of normal people in the occipital area and the orbitofrontal area is right-lateral, which is consistent with the right-lateral nature of the corresponding visual space and attention function of the area. Therefore, the study of the difference between the left and right hemispheric white matter is very important for us to explore the processing mechanism of brain cognitive function.
The brain network method based on the neuroimaging technology and the complex network theory provides an effective way for researching the connection mode between brain regions, and has been successfully used in the brain laterality research. By studying white matter networks inside the left and right hemispheres, some studies have demonstrated significant topological asymmetry between the two hemispheric networks of the brain, combining Diffusion Tensor Imaging (DTI) techniques and graph theory. For example, the right hemisphere network in healthy adults is significantly better in global efficiency than the left hemisphere network, indicating that the right hemisphere is significantly better in visual attention function than the left hemisphere.
Recently, a great deal of research has shown that brain networks possess the topological nature of a modular structure. The modular structure represents the aggregation degree of the network, and is one of the basic properties of the complex network. Each module is composed of partial nodes in a network, a human brain can be divided into a plurality of modules (mainly comprising a movement module, a vision module, an attention module, a default module and an edge system), each module corresponds to different brain functions, potential relations between brain structures and functions are explained, and a new visual angle is provided for exploring brain working mechanisms. The traditional laterality research about the brain structure network mainly explores the difference of the left and right hemisphere networks of the brain in the topological structure, and ignores the laterality of topological properties in network modules and among modules. Therefore, the laterality research based on module connection is more beneficial to researching the laterality of brain topological structure from the angle of local brain area connection and further researching the brain working mechanism.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for carrying out module division on a brain structure network based on a community division algorithm Louvain, and based on a divided module, detecting the asymmetry of the structural connection between the interior of the module and the module by using a laterality index.
A deviation detection method of a brain structure network based on module connection is realized by adopting the following steps:
step S1: preprocessing the diffusion tensor imaging, and then performing region segmentation on the preprocessed diffusion tensor imaging according to the selected standardized brain atlas;
step S2: reconstructing fibers of the whole brain based on the tracking end condition by adopting a deterministic fiber bundle tracking algorithm, and calculating the fiber bundle quantity and partial anisotropy index of every two brain regions and the Surface area of each brain region, thereby obtaining a fiber bundle quantity matrix FN, a partial anisotropy index matrix FA and a brain region Surface area matrix Surface of the brain regions;
step S3: coupling the matrix FN with FA, and normalizing based on the Surface matrix to construct a brain structure network matrix SC;
step S4: dividing a brain structure network matrix SC into a left hemisphere network SCL and a right hemisphere network SCR;
step S5: respectively carrying out module division on two networks SCL and SCR based on a community division algorithm Louvain;
step S6: analyzing the structural connection inside each module and between the modules from the perspective of global efficiency, local efficiency, connection density and connection strength based on graph theory;
step S7: and calculating laterality indexes of connection indexes between the interior of each module and every two modules to obtain laterality analysis results of the brain structure network based on module connection.
In step S1, first, a decapsulation operation is performed on the DTI raw image using the BET toolbox in the FSL; then, using a FLIRT tool in the FSL to perform cephalic rectification, and using an eddy _ correct tool in the FSL to perform eddy current rectification; finally, carrying out tensor reconstruction on the DTI image by using PANDA software; according to the selected standardized brain atlas BN template, segmenting the preprocessed DTI image, and dividing the brain into 246 brain regions;
the PANDA software is a tool box which is developed by Beijing university of education and used for analyzing brain diffusion tensor imaging;
in step S2, the end conditions of fiber bundle tracing specifically include: 1) in the fiber bundle tracing process, if the partial anisotropy index of a certain fiber bundle reaches less than 0.1 when the certain fiber bundle reaches a certain cellulose, the tracing of the certain fiber bundle is terminated; 2) in the fiber bundle tracking process, if a certain fiber bundle reaches a certain voxel, the fiber bundle is positioned at the boundary of the cerebral cortex, and the tracking of the fiber bundle is terminated; 3) in the fiber bundle tracing process, if the deflection angle of a certain fiber bundle is more than 35 degrees when the certain fiber bundle tracing reaches a certain voxel, the tracing of the certain fiber bundle is terminated. Calculating the number of fiber bundles, partial anisotropy indexes and brain area Surface areas of every two brain areas based on the fiber bundle tracking result, thereby obtaining a fiber bundle number matrix FN, a partial anisotropy index matrix FA and a brain area Surface matrix Surface of the brain areas;
in step S3, the processing formula for constructing the structural brain network SC is as follows:
Figure BDA0002388332000000031
in formula (1), SCi,jAn element representing the ith row and the jth column in the brain structure network matrix SC; FAi,jAn element representing the ith row and the jth column in the partial anisotropy index matrix FA; FN (FN)i,jAn element indicating the ith row and the jth column in the fiber bundle number matrix FN; surfaceiRepresenting the surface area of the brain region i,. tau.representing the threshold value, taking the value 3, and the dimension of the brain structure network matrix SC being 246 × 246.
In step S4, the processing formula for dividing the brain structure network SC into the left and right hemispherical networks is as follows;
Figure BDA0002388332000000041
Figure BDA0002388332000000042
wherein the content of the first and second substances,
Figure BDA0002388332000000043
in formulas (2) and (3), SCi,jAn element representing the ith row and the jth column in the brain structure network matrix SC; SCLiL,jLAn element representing the iL row and the jL column in the left hemisphere structure network matrix SCL of the brain, wherein; SCR (Selective catalytic reduction)iR,jRAnd (3) elements in the iR row and the jR column in the network matrix SCR with the right hemisphere structure of the brain are shown.
In step S5, the Louvain algorithm is a community discovery algorithm based on modularity, and its optimization goal is to maximize the modularity of the entire community network. The method for carrying out module division on the brain structure network based on the community discovery algorithm Louvain comprises the following steps:
step S51: the modularity of the community network is a measurement method for evaluating the division quality of the community network, the modularity is the difference between the number of the connecting edges of the nodes in the community and the number of the edges under the random condition, the value range is (0,1), and the modularity is defined as follows:
Figure BDA0002388332000000044
in the formula, Wi,jRepresents the weight, k, between nodes i and jiRepresenting the degree of the node, m being the total number of connecting edges in the network, ciThe number of the community in which the node is located, ∑ in represents the sum of the weights of all edges in the community c, ∑ tot represents the sum of the weights of the edges connected to the node in the community ∑ tot.
Step S52, the method for partitioning the module by the community partitioning algorithm Louvain includes the steps that ① initially takes each vertex as a community, the number of the communities is the same as that of the vertices, ② sequentially merges each vertex and adjacent vertices, whether modularity gain delta Q of each vertex and the adjacent vertices is larger than 0 is calculated, if the modularity gain delta Q is larger than 0, the node is placed in the community where the adjacent node is located, ③ iterates the second step until the algorithm is stable, namely the communities where all the vertices belong do not change, ④ compresses all the nodes of each community into a node, the weight of the nodes in the community is converted into the weight of a new node ring, the weight of the nodes in the community is converted into the weight of a new node edge, and ⑤ repeats steps 1-3 until the algorithm is stable.
Figure BDA0002388332000000045
In step S6, analyzing the structural connection including global efficiency, local efficiency, connection density, and connection strength inside each module by using graph theory; the specific calculation formula of each index is as follows:
global efficiency (E)g):
Figure BDA0002388332000000051
Local efficiency (E)loc):
Figure BDA0002388332000000052
Connection density (D):
Figure BDA0002388332000000053
connection strength (S):
Figure BDA0002388332000000054
in formulas (6) to (9), H represents a hemispherical network; c represents a network of modules;
Figure BDA0002388332000000055
representing the number of nodes of a module c in the hemisphere H;
Figure BDA0002388332000000056
the structural connection number of the modules c in the hemisphere H is represented;
Figure BDA0002388332000000057
represents the connection density of modules c within hemisphere H;
Figure BDA0002388332000000058
represents the sum of all connection weights of the module c in the hemisphere H;
Figure BDA0002388332000000059
represents the connection strength of the module c in the hemisphere H; dijRepresenting the shortest path length between the node i and the node j;
Figure BDA00023883320000000510
indicating the global efficiency of module c within hemisphere H,
Figure BDA00023883320000000511
the local efficiency of module c within hemisphere H is shown.
In step S7, the asymmetry index is calculated as follows:
Figure BDA00023883320000000512
in the formula (10), GRGraph-theory connection index, G, representing the network of the right hemisphere structureLRepresents the left halfThe graph theory of the ball structure network is connected with indexes,
Figure BDA00023883320000000513
the graph theory connection index of the right intra-hemisphere module c is shown,
Figure BDA00023883320000000514
the graph theory connection index of the left hemisphere inner module c is shown.
Compared with the prior art, the invention has the beneficial effects that: compared with the traditional laterality detection method, the laterality detection method of the brain structure network based on module connection, disclosed by the invention, fuses network modularity and laterality, and can more effectively and accurately mine laterality of local network indexes of the brain, thereby providing certain help for exploring a brain working mechanism.
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FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments.
Examples
(1) Experimental data
The experiment was performed by selecting 52 Normal persons (Normal control, NC) as subjects.
(2) Procedure of experiment
Referring to fig. 1, according to the process shown in fig. 1, the laterality of the brain structural network of 52 tested subjects is analyzed by the following steps:
step S1: preprocessing the diffusion tensor imaging, and then performing region segmentation on the preprocessed diffusion tensor imaging according to the selected standardized brain atlas;
in step S1, brain function magnetic resonance imaging Software (FSL) is used for the preprocessing, which specifically includes: magnetic coefficient correction, eddy current distortion correction and head movement correction; the standardized Brain atlas is an International Consortium of Brain Imaging (ICBM) atlas; segmenting the preprocessed diffusion tensor image, and dividing the brain into 246 brain regions;
step S2: reconstructing fibers of the whole brain based on the tracking end condition by adopting a deterministic fiber bundle tracking algorithm, and calculating the fiber bundle quantity, the partial anisotropy index and the Surface area of each brain area in every two brain areas, thereby obtaining a fiber bundle quantity matrix FN, a partial anisotropy index matrix FA and a Surface area matrix Surface in the brain areas;
in step S2, the deterministic fiber bundle tracking algorithm employs any one of the following four algorithms: fiber Association Continuous Tracking (FACT), 2nd order runge-kutta (2nd order RK), Tensoline and interleaved Streamline; the end conditions for fiber bundle tracing specifically include: 1) in the fiber bundle tracing process, if the partial anisotropy index of a certain fiber bundle reaches less than 0.1 when the certain fiber bundle reaches a certain cellulose, the tracing of the certain fiber bundle is terminated; 2) in the fiber bundle tracking process, if a certain fiber bundle reaches a certain voxel, the fiber bundle is positioned at the boundary of the cerebral cortex, and the tracking of the fiber bundle is terminated; 3) in the fiber bundle tracing process, if the deflection angle of a certain fiber bundle is more than 35 degrees when the certain fiber bundle tracing reaches a certain voxel, the tracing of the certain fiber bundle is terminated.
Step S3: coupling the matrix FN with FA, and normalizing based on the Surface matrix to construct a brain structure network matrix SC;
in step S3, the connection between the two brain regions is calculated using formula (1):
Figure BDA0002388332000000071
in formula (1), SCi,jAn element representing the ith row and the jth column in the brain structure network matrix SC; FAi,jAn element representing the ith row and the jth column in the partial anisotropy index matrix FA; FN (FN)i,jAn element indicating the ith row and the jth column in the fiber bundle number matrix FN; surfaceiRepresents the surface area of brain region i; τ represents a threshold value, takenThe value is 3 and the dimension of the brain structure network matrix SC is 246 × 246.
Step S4: dividing a brain structure network SC into a left hemisphere network SCL and a right hemisphere network SCR;
in step S4, the left hemisphere network SCL is constructed using formula (2), and the right hemisphere network SCR is constructed using formula (3):
Figure BDA0002388332000000072
Figure BDA0002388332000000073
wherein the content of the first and second substances,
Figure BDA0002388332000000074
in formulas (2) and (3), SCi,jAn element representing the ith row and the jth column in the brain structure network matrix SC; SCLiL,jLAn element representing the iL row and the jL column in the left hemisphere structure network matrix SCL of the brain, wherein; SCR (Selective catalytic reduction)iR,jRAnd (3) elements in the iR row and the jR column in the network matrix SCR with the right hemisphere structure of the brain are shown.
Step S5: respectively carrying out module division on two networks SCL and SCR based on a community division algorithm Louvain;
in step S5, the Louvain algorithm is a community discovery algorithm based on modularity, and its optimization goal is to maximize the modularity of the entire community network. The method for carrying out module division on the basis of the community discovery algorithm Louvain brain structure network comprises the following steps:
step S51: the modularity of the community network is a measurement method for evaluating the division quality of the community network, the modularity is the difference between the number of the connecting edges of the nodes in the community and the number of the edges under the random condition, the value range is (0,1), and the modularity is defined as follows:
Figure BDA0002388332000000081
in the formula, Wi,jRepresents the weight, k, between nodes i and jiRepresenting nodesM is the total number of connecting edges in the network, ciThe number of the community in which the node is located, ∑ in represents the sum of the weights of all edges in the community c, ∑ tot represents the sum of the weights of the edges connected to the node in the community ∑ tot.
Step S52, the method for partitioning the module by the community partitioning algorithm Louvain includes the steps that ① initially takes each vertex as a community, the number of the communities is the same as that of the vertices, ② sequentially merges each vertex and adjacent vertices, whether modularity gain delta Q of each vertex and the adjacent vertices is larger than 0 is calculated, if the modularity gain delta Q is larger than 0, the node is placed in the community where the adjacent node is located, ③ iterates the second step until the algorithm is stable, namely the communities where all the vertices belong do not change, ④ compresses all the nodes of each community into a node, the weight of the nodes in the community is converted into the weight of a new node ring, the weight of the nodes in the community is converted into the weight of a new node edge, and ⑤ repeats steps 1-3 until the algorithm is stable.
Figure BDA0002388332000000082
Step S6: analyzing the structural connection inside each module by using graph theory, wherein the structural connection comprises global efficiency, local efficiency, connection density and connection strength;
in step S6, analyzing the structural connection including global efficiency, local efficiency, connection density, and connection strength inside each module by using graph theory; the specific calculation formula of each index is as follows:
global efficiency (E)g):
Figure BDA0002388332000000083
Local efficiency (E)loc):
Figure BDA0002388332000000084
Connection density (D):
Figure BDA0002388332000000085
connection strength (S):
Figure BDA0002388332000000086
in formulas (6) to (9), H represents a hemispherical network; c represents a network of modules;
Figure BDA0002388332000000087
representing the number of nodes of a module c in the hemisphere H;
Figure BDA0002388332000000091
the structural connection number of the modules c in the hemisphere H is represented;
Figure BDA0002388332000000092
represents the connection density of modules c within hemisphere H;
Figure BDA0002388332000000093
represents the sum of all connection weights of the module c in the hemisphere H;
Figure BDA0002388332000000094
represents the connection strength of the module c in the hemisphere H; dijRepresenting the shortest path length between the node i and the node j;
Figure BDA0002388332000000095
indicating the global efficiency of module c within hemisphere H,
Figure BDA0002388332000000096
the local efficiency of module c within hemisphere H is shown.
Step S7: and calculating laterality indexes of connection indexes between the interior of each module and every two modules to obtain laterality analysis results of the brain structure network based on module connection.
In step S7, the laterality index is calculated using equation (10):
Figure BDA0002388332000000097
in the formula (10), GRDiagram showing right hemisphere structure networkTheoretical connection index, GLA graph theory connection index representing the left hemisphere structure network,
Figure BDA0002388332000000098
the graph theory connection index of the right intra-hemisphere module c is shown,
Figure BDA0002388332000000099
the graph theory connection index of the left hemisphere inner module c is shown.
(3) Results of the experiment
TABLE 1 index of significant laterality between hemisphere network interior and module interior structural connection
Figure BDA00023883320000000910
TABLE 2 significant laterality index for structural connections between modules
Figure BDA00023883320000000911
Figure BDA0002388332000000101
Compared with the traditional laterality detection method, the laterality detection method of the brain structure network based on module connection, disclosed by the invention, fuses network modularity and laterality, and can more effectively and accurately mine laterality of local network indexes of the brain, thereby providing certain help for exploring a brain working mechanism.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A deviation detection method of a brain structure network based on module connection is characterized by comprising the following steps:
step S1: preprocessing the diffusion tensor imaging, and performing region segmentation on the preprocessed diffusion tensor imaging according to the selected standardized brain atlas;
step S2: reconstructing fibers of the whole brain based on the tracking end condition by adopting a deterministic fiber bundle tracking algorithm, and calculating the fiber bundle quantity and partial anisotropy index of every two brain regions and the Surface area of each brain region to obtain a fiber bundle quantity matrix FN, a partial anisotropy index matrix FA and a brain region Surface area matrix Surface of the brain regions;
step S3: coupling the matrix FN with FA, and normalizing based on the Surface matrix to construct a brain structure network matrix SC;
step S4: dividing a brain structure network matrix SC into a left hemisphere network SCL and a right hemisphere network SCR;
step S5: respectively carrying out module division on two networks SCL and SCR based on a community division algorithm Louvain;
step S6: analyzing the structural connection inside each module and between the modules from the perspective of global efficiency, local efficiency, connection density and connection strength based on graph theory;
step S7: and calculating laterality indexes of connection indexes between the interior of each module and every two modules to obtain laterality analysis results of the brain structure network based on module connection.
2. The detection method according to claim 1, wherein in step S1, the method specifically includes: firstly, a BET toolbox in the FSL is utilized to perform a head removing operation on an original image of diffusion tensor imaging; then, using a FLIRT tool in the FSL to perform cephalic rectification, and using an eddy _ correct tool in the FSL to perform eddy current rectification; finally, carrying out tensor reconstruction on the diffusion tensor imaging image by using PANDA software; and (3) according to the selected standardized brain atlas, segmenting the preprocessed diffusion tensor imaging image to divide the brain into 246 brain regions.
3. The detection method according to claim 2, wherein in step S2, the end condition of the fiber bundle tracking specifically includes: 1) in the fiber bundle tracing process, if the partial anisotropy index of a certain fiber bundle reaches less than 0.1 when the certain fiber bundle reaches a certain cellulose, the tracing of the certain fiber bundle is terminated; 2) in the fiber bundle tracking process, if a certain fiber bundle reaches a certain voxel, the fiber bundle is positioned at the boundary of the cerebral cortex, and the tracking of the fiber bundle is terminated; 3) in the fiber bundle tracing process, if the deflection angle of a certain fiber bundle is more than 35 degrees when the certain fiber bundle tracing reaches a certain voxel, the tracing of the certain fiber bundle is terminated.
4. The detection method according to claim 3, wherein in step S3, the processing formula for constructing the structural brain network SC is as follows:
Figure FDA0002388331990000021
in formula (1), SCi,jAn element representing the ith row and the jth column in the brain structure network matrix SC; FAi,jAn element representing the ith row and the jth column in the partial anisotropy index matrix FA; FN (FN)i,jAn element indicating the ith row and the jth column in the fiber bundle number matrix FN; surfaceiRepresenting the surface area of the brain region i,. tau.representing the threshold value, taking the value 3, and the dimension of the brain structure network matrix SC being 246 × 246.
5. The detecting method according to claim 4, wherein in step S4, the processing formula for dividing the brain structure network SC into two left and right hemisphere networks is as follows;
Figure FDA0002388331990000022
Figure FDA0002388331990000023
wherein the content of the first and second substances,
Figure FDA0002388331990000024
in formulas (2) and (3), SCi,jAn element representing the ith row and the jth column in the brain structure network matrix SC; SCLiL,jLAn element representing the iL row and the jL column in the left hemisphere structure network matrix SCL of the brain, wherein; SCR (Selective catalytic reduction)iR,jRAnd (3) elements in the iR row and the jR column in the network matrix SCR with the right hemisphere structure of the brain are shown.
6. The detection method according to claim 5, wherein in step S5, the module division of the brain structure network based on the community discovery algorithm Louvain comprises the following steps:
step S51: the modularity of the community network is the difference between the number of the connecting edges of the nodes in the community and the number of the connecting edges under random conditions, the value range of the modularity is (0,1), and the modularity is defined as follows:
Figure FDA0002388331990000031
in the formula, Wi,jRepresents the weight, k, between nodes i and jiRepresenting the degree of the node, m being the total number of connecting edges in the network, ci∑ in represents the sum of the weights of all edges in the community c, and ∑ tot represents the sum of the weights of the edges connected with the nodes in the community ∑ tot;
step S52, the method for the community partition algorithm Louvain partition module comprises the following steps:
① initially regarding each vertex as a community, wherein the number of communities is the same as that of the vertices;
② combining each vertex with its adjacent vertex in turn, calculating whether their modularity gain Δ Q is greater than 0, if so, putting the node into the community of the adjacent node, the calculation formula is as follows:
Figure FDA0002388331990000032
③, iterating the second step until the algorithm is stable, that is, the communities to which all the vertexes belong do not change;
④ compressing all the nodes in each community into a node, converting the weight of the node in the community into the weight of a new node ring, and converting the weight between communities into the weight of a new node edge;
⑤ repeat steps ① - ③ until the algorithm stabilizes.
7. The method for detecting according to claim 6, wherein in step S6, the structural connection inside each module is analyzed by graph theory, including global efficiency, local efficiency, connection density, connection strength; the specific calculation formula of each index is as follows:
global efficiency (E)g):
Figure FDA0002388331990000033
Local efficiency (E)loc):
Figure FDA0002388331990000034
Connection density (D):
Figure FDA0002388331990000035
connection strength (S):
Figure FDA0002388331990000036
in formulas (6) to (9), H represents a hemispherical network; c represents a network of modules;
Figure FDA0002388331990000041
representing the number of nodes of a module c in the hemisphere H;
Figure FDA0002388331990000042
the structural connection number of the modules c in the hemisphere H is represented;
Figure FDA0002388331990000043
showing the connection of module c within hemisphere HDensity;
Figure FDA0002388331990000044
represents the sum of all connection weights of the module c in the hemisphere H;
Figure FDA0002388331990000045
represents the connection strength of the module c in the hemisphere H; dijRepresenting the shortest path length between the node i and the node j;
Figure FDA0002388331990000046
indicating the global efficiency of module c within hemisphere H,
Figure FDA0002388331990000047
the local efficiency of module c within hemisphere H is shown.
8. The detection method according to claim 7, wherein in step S7, the laterality index is calculated as follows:
Figure FDA0002388331990000048
in the formula (10), GRGraph-theory connection index, G, representing the network of the right hemisphere structureLA graph theory connection index representing the left hemisphere structure network,
Figure FDA0002388331990000049
the graph theory connection index of the right intra-hemisphere module c is shown,
Figure FDA00023883319900000410
the graph theory connection index of the left hemisphere inner module c is shown.
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